Overview

Dataset statistics

Number of variables15
Number of observations8281
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory970.6 KiB
Average record size in memory120.0 B

Variable types

Numeric14
Categorical1

Alerts

time(s) is highly correlated with S and 13 other fieldsHigh correlation
T1 is highly correlated with time(s) and 13 other fieldsHigh correlation
T2 is highly correlated with time(s) and 13 other fieldsHigh correlation
T3 is highly correlated with time(s) and 13 other fieldsHigh correlation
T4 is highly correlated with time(s) and 13 other fieldsHigh correlation
T5 is highly correlated with time(s) and 13 other fieldsHigh correlation
T6 is highly correlated with time(s) and 12 other fieldsHigh correlation
T7 is highly correlated with time(s) and 13 other fieldsHigh correlation
T8 is highly correlated with time(s) and 13 other fieldsHigh correlation
T9 is highly correlated with time(s) and 13 other fieldsHigh correlation
T10 is highly correlated with time(s) and 13 other fieldsHigh correlation
T11 is highly correlated with time(s) and 13 other fieldsHigh correlation
T12 is highly correlated with time(s) and 13 other fieldsHigh correlation
Z is highly correlated with time(s) and 13 other fieldsHigh correlation
S is highly correlated with time(s) and 12 other fieldsHigh correlation
time(s) is uniformly distributed Uniform
time(s) has unique values Unique

Reproduction

Analysis started2022-11-11 03:25:58.454866
Analysis finished2022-11-11 03:26:09.333179
Duration10.88 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

time(s)
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct8281
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345
Minimum0
Maximum690
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:09.360727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.5
Q1172.5
median345
Q3517.5
95-th percentile655.5
Maximum690
Range690
Interquartile range (IQR)345

Descriptive statistics

Standard deviation199.2219269
Coefficient of variation (CV)0.5774548606
Kurtosis-1.2
Mean345
Median Absolute Deviation (MAD)172.5
Skewness-1.019908669 × 10-16
Sum2856945
Variance39689.37616
MonotonicityStrictly increasing
2022-11-11T11:26:09.418497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
458.33333331
 
< 0.1%
460.83333331
 
< 0.1%
460.751
 
< 0.1%
460.66666671
 
< 0.1%
460.58333331
 
< 0.1%
460.51
 
< 0.1%
460.41666671
 
< 0.1%
460.33333331
 
< 0.1%
460.251
 
< 0.1%
Other values (8271)8271
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
ValueCountFrequency (%)
6901
< 0.1%
689.91666671
< 0.1%
689.83333331
< 0.1%
689.751
< 0.1%
689.66666671
< 0.1%
689.58333331
< 0.1%
689.51
< 0.1%
689.41666671
< 0.1%
689.33333331
< 0.1%
689.251
< 0.1%

S
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size64.8 KiB
24000
4287 
0
3706 
12000
 
141
3000
 
75
20000
 
72

Length

Max length5
Median length5
Mean length3.200821157
Min length1

Characters and Unicode

Total characters26506
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000
2nd row3000
3rd row3000
4th row3000
5th row3000

Common Values

ValueCountFrequency (%)
240004287
51.8%
03706
44.8%
12000141
 
1.7%
300075
 
0.9%
2000072
 
0.9%

Length

2022-11-11T11:26:09.476301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-11T11:26:09.529124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
240004287
51.8%
03706
44.8%
12000141
 
1.7%
300075
 
0.9%
2000072
 
0.9%

Most occurring characters

ValueCountFrequency (%)
017503
66.0%
24500
 
17.0%
44287
 
16.2%
1141
 
0.5%
375
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26506
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
017503
66.0%
24500
 
17.0%
44287
 
16.2%
1141
 
0.5%
375
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common26506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
017503
66.0%
24500
 
17.0%
44287
 
16.2%
1141
 
0.5%
375
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII26506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
017503
66.0%
24500
 
17.0%
44287
 
16.2%
1141
 
0.5%
375
 
0.3%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.3255887
Minimum22.5
Maximum27.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:09.578956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile23.1
Q123.5
median26.5
Q327.1
95-th percentile27.4
Maximum27.4
Range4.9
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation1.792235089
Coefficient of variation (CV)0.07076775628
Kurtosis-1.860348112
Mean25.3255887
Median Absolute Deviation (MAD)0.9
Skewness-0.1135568789
Sum209721.2
Variance3.212106613
MonotonicityNot monotonic
2022-11-11T11:26:09.635676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.1944
11.4%
27.3849
10.3%
27.1691
 
8.3%
23.5621
 
7.5%
23.4606
 
7.3%
27583
 
7.0%
27.4560
 
6.8%
26.7475
 
5.7%
23.8466
 
5.6%
26.6388
 
4.7%
Other values (89)2098
25.3%
ValueCountFrequency (%)
22.538
0.5%
22.551
 
< 0.1%
22.62
 
< 0.1%
22.651
 
< 0.1%
22.72
 
< 0.1%
22.751
 
< 0.1%
22.81
 
< 0.1%
22.851
 
< 0.1%
22.95
 
0.1%
22.951
 
< 0.1%
ValueCountFrequency (%)
27.4560
6.8%
27.354
 
< 0.1%
27.3849
10.3%
27.252
 
< 0.1%
27.2126
 
1.5%
27.156
 
0.1%
27.1691
8.3%
27.055
 
0.1%
27583
7.0%
26.953
 
< 0.1%

T2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.04726482
Minimum21.9
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:09.684142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.9
5-th percentile22.3
Q122.8
median23.1
Q323.4
95-th percentile23.8
Maximum23.8
Range1.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.4238796754
Coefficient of variation (CV)0.01839175619
Kurtosis-0.1680552703
Mean23.04726482
Median Absolute Deviation (MAD)0.3
Skewness-0.3583737783
Sum190854.4
Variance0.1796739793
MonotonicityNot monotonic
2022-11-11T11:26:09.729736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
23.11819
22.0%
23.41090
13.2%
22.9674
 
8.1%
23636
 
7.7%
23.8565
 
6.8%
23.3403
 
4.9%
23.5399
 
4.8%
22.5374
 
4.5%
22.7338
 
4.1%
22.8314
 
3.8%
Other values (10)1669
20.2%
ValueCountFrequency (%)
21.960
 
0.7%
2273
 
0.9%
22.184
 
1.0%
22.2155
1.9%
22.3194
2.3%
22.4305
3.7%
22.5374
4.5%
22.6300
3.6%
22.7338
4.1%
22.8314
3.8%
ValueCountFrequency (%)
23.8565
 
6.8%
23.7183
 
2.2%
23.615
 
0.2%
23.5399
 
4.8%
23.41090
13.2%
23.3403
 
4.9%
23.2300
 
3.6%
23.11819
22.0%
23636
 
7.7%
22.9674
 
8.1%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.91912812
Minimum21.7
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:09.778845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile22
Q122.6
median23
Q323.5
95-th percentile23.8
Maximum23.8
Range2.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.5359210439
Coefficient of variation (CV)0.02338313399
Kurtosis-0.7704339822
Mean22.91912812
Median Absolute Deviation (MAD)0.4
Skewness-0.1755637493
Sum189793.3
Variance0.2872113653
MonotonicityNot monotonic
2022-11-11T11:26:09.821702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
23.11480
17.9%
23.51397
16.9%
22.7701
8.5%
23.8685
8.3%
22.6666
8.0%
23467
 
5.6%
22.9421
 
5.1%
22.3354
 
4.3%
22.1353
 
4.3%
22.8348
 
4.2%
Other values (12)1409
17.0%
ValueCountFrequency (%)
21.791
 
1.1%
21.868
 
0.8%
21.9129
 
1.6%
22221
 
2.7%
22.1353
4.3%
22.2264
 
3.2%
22.3354
4.3%
22.4232
 
2.8%
22.5193
 
2.3%
22.6666
8.0%
ValueCountFrequency (%)
23.8685
8.3%
23.764
 
0.8%
23.613
 
0.2%
23.51397
16.9%
23.486
 
1.0%
23.328
 
0.3%
23.220
 
0.2%
23.11480
17.9%
23467
 
5.6%
22.9421
 
5.1%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.74966791
Minimum22.5
Maximum24.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:09.867786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile23.2
Q123.5
median23.8
Q324.1
95-th percentile24.4
Maximum24.5
Range2
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.4227700216
Coefficient of variation (CV)0.01780109192
Kurtosis-0.2276269474
Mean23.74966791
Median Absolute Deviation (MAD)0.3
Skewness-0.2694601934
Sum196671
Variance0.1787344912
MonotonicityNot monotonic
2022-11-11T11:26:09.913934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
23.5861
10.4%
23.9834
10.1%
23.2670
 
8.1%
23.6639
 
7.7%
24.2636
 
7.7%
23.4568
 
6.9%
23.7565
 
6.8%
24.1552
 
6.7%
23.8528
 
6.4%
24522
 
6.3%
Other values (11)1906
23.0%
ValueCountFrequency (%)
22.596
 
1.2%
22.630
 
0.4%
22.731
 
0.4%
22.835
 
0.4%
22.944
 
0.5%
2352
 
0.6%
23.165
 
0.8%
23.2670
8.1%
23.3469
5.7%
23.4568
6.9%
ValueCountFrequency (%)
24.5328
 
4.0%
24.4307
 
3.7%
24.3449
5.4%
24.2636
7.7%
24.1552
6.7%
24522
6.3%
23.9834
10.1%
23.8528
6.4%
23.7565
6.8%
23.6639
7.7%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.48728414
Minimum22.7
Maximum23.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:09.958785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.7
5-th percentile22.7
Q123.4
median23.5
Q323.7
95-th percentile23.9
Maximum23.9
Range1.2
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.3077601449
Coefficient of variation (CV)0.01310326656
Kurtosis0.6971907981
Mean23.48728414
Median Absolute Deviation (MAD)0.2
Skewness-1.013558853
Sum194498.2
Variance0.09471630681
MonotonicityNot monotonic
2022-11-11T11:26:10.002933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
23.51551
18.7%
23.71351
16.3%
23.61172
14.2%
23.41159
14.0%
23.9987
11.9%
23.3514
 
6.2%
22.7443
 
5.3%
23.8301
 
3.6%
23174
 
2.1%
22.9167
 
2.0%
Other values (3)462
 
5.6%
ValueCountFrequency (%)
22.7443
 
5.3%
22.8156
 
1.9%
22.9167
 
2.0%
23174
 
2.1%
23.1165
 
2.0%
23.2141
 
1.7%
23.3514
 
6.2%
23.41159
14.0%
23.51551
18.7%
23.61172
14.2%
ValueCountFrequency (%)
23.9987
11.9%
23.8301
 
3.6%
23.71351
16.3%
23.61172
14.2%
23.51551
18.7%
23.41159
14.0%
23.3514
 
6.2%
23.2141
 
1.7%
23.1165
 
2.0%
23174
 
2.1%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.24934187
Minimum23
Maximum23.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:10.107583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile23
Q123.1
median23.2
Q323.4
95-th percentile23.5
Maximum23.6
Range0.6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.187211586
Coefficient of variation (CV)0.00805233916
Kurtosis-1.20462022
Mean23.24934187
Median Absolute Deviation (MAD)0.2
Skewness0.08690609171
Sum192527.8
Variance0.03504817792
MonotonicityNot monotonic
2022-11-11T11:26:10.146452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
231949
23.5%
23.21517
18.3%
23.31252
15.1%
23.41250
15.1%
23.51240
15.0%
23.1756
 
9.1%
23.6317
 
3.8%
ValueCountFrequency (%)
231949
23.5%
23.1756
 
9.1%
23.21517
18.3%
23.31252
15.1%
23.41250
15.1%
23.51240
15.0%
23.6317
 
3.8%
ValueCountFrequency (%)
23.6317
 
3.8%
23.51240
15.0%
23.41250
15.1%
23.31252
15.1%
23.21517
18.3%
23.1756
 
9.1%
231949
23.5%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.81415288
Minimum21.7
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:10.192297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile21.9
Q122.5
median22.8
Q323.4
95-th percentile23.7
Maximum23.7
Range2
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.5461327901
Coefficient of variation (CV)0.02393833306
Kurtosis-0.9479088588
Mean22.81415288
Median Absolute Deviation (MAD)0.4
Skewness-0.1657050154
Sum188924
Variance0.2982610245
MonotonicityNot monotonic
2022-11-11T11:26:10.237849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
23.41378
16.6%
23.11286
15.5%
22.5776
9.4%
22.6698
 
8.4%
23.7643
 
7.8%
22.7421
 
5.1%
22.2366
 
4.4%
22.8339
 
4.1%
22310
 
3.7%
22.1299
 
3.6%
Other values (11)1765
21.3%
ValueCountFrequency (%)
21.7163
 
2.0%
21.8202
 
2.4%
21.9204
 
2.5%
22310
 
3.7%
22.1299
 
3.6%
22.2366
4.4%
22.3219
 
2.6%
22.4275
 
3.3%
22.5776
9.4%
22.6698
8.4%
ValueCountFrequency (%)
23.7643
7.8%
23.670
 
0.8%
23.526
 
0.3%
23.41378
16.6%
23.388
 
1.1%
23.242
 
0.5%
23.11286
15.5%
23191
 
2.3%
22.9285
 
3.4%
22.8339
 
4.1%

T8
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.09978263
Minimum22.2
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:10.285713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.2
5-th percentile22.4
Q123
median23.1
Q323.4
95-th percentile23.7
Maximum23.7
Range1.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3647876109
Coefficient of variation (CV)0.01579182006
Kurtosis-0.2349025793
Mean23.09978263
Median Absolute Deviation (MAD)0.3
Skewness-0.5073786206
Sum191289.3
Variance0.1330700011
MonotonicityNot monotonic
2022-11-11T11:26:10.327731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
23.41881
22.7%
23.11854
22.4%
23887
10.7%
23.7678
 
8.2%
22.5423
 
5.1%
23.2411
 
5.0%
23.3393
 
4.7%
22.8289
 
3.5%
22.7275
 
3.3%
22.9273
 
3.3%
Other values (6)917
11.1%
ValueCountFrequency (%)
22.2128
 
1.5%
22.3175
 
2.1%
22.4245
 
3.0%
22.5423
 
5.1%
22.6255
 
3.1%
22.7275
 
3.3%
22.8289
 
3.5%
22.9273
 
3.3%
23887
10.7%
23.11854
22.4%
ValueCountFrequency (%)
23.7678
 
8.2%
23.698
 
1.2%
23.516
 
0.2%
23.41881
22.7%
23.3393
 
4.7%
23.2411
 
5.0%
23.11854
22.4%
23887
10.7%
22.9273
 
3.3%
22.8289
 
3.5%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct79
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.72550417
Minimum25.5
Maximum29.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:10.383753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25.5
5-th percentile26.6
Q126.9
median27.7
Q328.6
95-th percentile29.1
Maximum29.4
Range3.9
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation0.8968583465
Coefficient of variation (CV)0.03234777413
Kurtosis-1.293335357
Mean27.72550417
Median Absolute Deviation (MAD)0.9
Skewness0.01343425436
Sum229594.9
Variance0.8043548937
MonotonicityNot monotonic
2022-11-11T11:26:10.442836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.8778
 
9.4%
26.9582
 
7.0%
26.7475
 
5.7%
28.4413
 
5.0%
28.5395
 
4.8%
28.6393
 
4.7%
28.7389
 
4.7%
26.6347
 
4.2%
28.9319
 
3.9%
27.3294
 
3.6%
Other values (69)3896
47.0%
ValueCountFrequency (%)
25.526
0.3%
25.553
 
< 0.1%
25.638
0.5%
25.651
 
< 0.1%
25.79
 
0.1%
25.751
 
< 0.1%
25.85
 
0.1%
25.851
 
< 0.1%
25.97
 
0.1%
25.951
 
< 0.1%
ValueCountFrequency (%)
29.430
 
0.4%
29.352
 
< 0.1%
29.362
 
0.7%
29.256
 
0.1%
29.255
 
0.7%
29.1521
 
0.3%
29.1248
3.0%
29.0527
 
0.3%
29183
2.2%
28.9514
 
0.2%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.3353339
Minimum26
Maximum30.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:10.505624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile26.7
Q127
median28.5
Q329.5
95-th percentile29.9
Maximum30.4
Range4.4
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.224869179
Coefficient of variation (CV)0.04322762468
Kurtosis-1.542497621
Mean28.3353339
Median Absolute Deviation (MAD)1.2
Skewness-0.06866353885
Sum234644.9
Variance1.500304505
MonotonicityNot monotonic
2022-11-11T11:26:10.560439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
26.8772
 
9.3%
29.8471
 
5.7%
26.9441
 
5.3%
29.9419
 
5.1%
29.7411
 
5.0%
26.7400
 
4.8%
29.4335
 
4.0%
29.5320
 
3.9%
27311
 
3.8%
29.6289
 
3.5%
Other values (35)4112
49.7%
ValueCountFrequency (%)
2661
 
0.7%
26.122
 
0.3%
26.27
 
0.1%
26.32
 
< 0.1%
26.43
 
< 0.1%
26.56
 
0.1%
26.691
 
1.1%
26.7400
4.8%
26.8772
9.3%
26.9441
5.3%
ValueCountFrequency (%)
30.41
 
< 0.1%
30.342
 
0.5%
30.274
 
0.9%
30.1121
 
1.5%
30175
 
2.1%
29.9419
5.1%
29.8471
5.7%
29.7411
5.0%
29.6289
3.5%
29.5320
3.9%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.87022099
Minimum21.7
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:10.611268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile21.9
Q122.4
median22.9
Q323.4
95-th percentile23.6
Maximum23.8
Range2.1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5560749801
Coefficient of variation (CV)0.02431436847
Kurtosis-1.221854554
Mean22.87022099
Median Absolute Deviation (MAD)0.5
Skewness-0.1831300547
Sum189388.3
Variance0.3092193835
MonotonicityNot monotonic
2022-11-11T11:26:10.653613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
23.4698
 
8.4%
23.5621
 
7.5%
22.2604
 
7.3%
23.6576
 
7.0%
23.3568
 
6.9%
22.4459
 
5.5%
22.8438
 
5.3%
22.3417
 
5.0%
23.2417
 
5.0%
22.5391
 
4.7%
Other values (12)3092
37.3%
ValueCountFrequency (%)
21.72
 
< 0.1%
21.8141
 
1.7%
21.9299
3.6%
22219
 
2.6%
22.1249
3.0%
22.2604
7.3%
22.3417
5.0%
22.4459
5.5%
22.5391
4.7%
22.6375
4.5%
ValueCountFrequency (%)
23.882
 
1.0%
23.7295
3.6%
23.6576
7.0%
23.5621
7.5%
23.4698
8.4%
23.3568
6.9%
23.2417
5.0%
23.1385
4.6%
23303
3.7%
22.9378
4.6%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.80026567
Minimum21.7
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:10.699339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile22.2
Q122.4
median22.8
Q323.2
95-th percentile23.5
Maximum23.7
Range2
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.474035942
Coefficient of variation (CV)0.02079080783
Kurtosis-1.058911335
Mean22.80026567
Median Absolute Deviation (MAD)0.4
Skewness-0.1095682923
Sum188809
Variance0.2247100743
MonotonicityNot monotonic
2022-11-11T11:26:10.744151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
22.3961
11.6%
23.3771
9.3%
22.2729
 
8.8%
22.4724
 
8.7%
23.4644
 
7.8%
23.1632
 
7.6%
22.9559
 
6.8%
23.2527
 
6.4%
22.7484
 
5.8%
22.5413
 
5.0%
Other values (11)1837
22.2%
ValueCountFrequency (%)
21.7136
 
1.6%
21.848
 
0.6%
21.945
 
0.5%
2223
 
0.3%
22.146
 
0.6%
22.2729
8.8%
22.3961
11.6%
22.4724
8.7%
22.5413
5.0%
22.6259
 
3.1%
ValueCountFrequency (%)
23.751
 
0.6%
23.6187
 
2.3%
23.5318
3.8%
23.4644
7.8%
23.3771
9.3%
23.2527
6.4%
23.1632
7.6%
23381
4.6%
22.9559
6.8%
22.8343
4.1%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct149
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.70975383
Minimum0
Maximum84.094
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2022-11-11T11:26:10.800867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.531
Q125.594
median67.031
Q381.656
95-th percentile82.875
Maximum84.094
Range84.094
Interquartile range (IQR)56.062

Descriptive statistics

Standard deviation28.95902377
Coefficient of variation (CV)0.5293210395
Kurtosis-1.525031448
Mean54.70975383
Median Absolute Deviation (MAD)15.844
Skewness-0.3768718026
Sum453051.4715
Variance838.6250579
MonotonicityNot monotonic
2022-11-11T11:26:10.856746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.8751341
 
16.2%
80.4371050
 
12.7%
81.656788
 
9.5%
84.094272
 
3.3%
8.531209
 
2.5%
10.969154
 
1.9%
9.75149
 
1.8%
18.281134
 
1.6%
81.0465122
 
1.5%
25.594109
 
1.3%
Other values (139)3953
47.7%
ValueCountFrequency (%)
09
 
0.1%
1.21851
 
< 0.1%
2.4371
 
< 0.1%
3.04651
 
< 0.1%
3.6562
 
< 0.1%
4.26552
 
< 0.1%
4.87535
 
0.4%
5.484517
 
0.2%
6.094102
1.2%
6.70313
 
0.2%
ValueCountFrequency (%)
84.094272
 
3.3%
83.484567
 
0.8%
82.8751341
16.2%
82.265579
 
1.0%
81.656788
9.5%
81.0465122
 
1.5%
80.4371050
12.7%
79.82844
 
0.5%
79.21994
 
1.1%
78.609512
 
0.1%

Interactions

2022-11-11T11:26:08.435400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:58.955355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.666602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.386803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.100030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.814678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.604636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.285229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.041811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.767067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.540788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.260787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.991983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.645575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.483793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.009174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.715377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.438628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.147118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.867952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.654520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.335781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.094440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.818838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.592956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.308723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.040721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.762156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.527106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.056398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.759063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.486689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.190040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.915791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.699723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.381443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.143276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.866965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.641330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.353349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.084573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.810588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.576164image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.108856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.808338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.538515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.302835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.969665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.750551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.433124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.197615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.918637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.695141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.474940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.133189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.864407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.619020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.154701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.849878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.586353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.344396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.018117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.794457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.478976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.245152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.964991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.743077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.518865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.175432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.912006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.668948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.208578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.901330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.640720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.394399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.073900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.846948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.531996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.300023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.019423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.797892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.569037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.224956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.966308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.715693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.257864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.006649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.690517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.440136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.124302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.893791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.580556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.350646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.069588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.847776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.613830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.269774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.018073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.763088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.309505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.055493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.741944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.486978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.176493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.943762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.630368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.402471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.184896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.899602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.661720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.317579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.071903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.813529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.364316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.105325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.795763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.535896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.231308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.995809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.683190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.458283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.239681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.955051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.711663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.367410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.126364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.862570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.417619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.154989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.849628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.585988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.285180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.046638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.734411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.511726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.291391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.008923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.760920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.415249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.179947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.912084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.471585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.205017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.903422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.635820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.339447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.097521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.786863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.566801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.344991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.063976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.810752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.465086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.235810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.956007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.518516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.249808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.951185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.678747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.388282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.143619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.897258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.616656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.392639image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.110818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.854604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.507942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.285051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:09.062006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.564678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.292727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.999024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.721006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.435747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.188468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.943544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.664270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.439218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.157712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.898496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.550798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.331893image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:09.112214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:25:59.618553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:00.342986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.053307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:01.770832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:02.491089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.240348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:03.996962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:04.719387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:05.493833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.212949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:06.949181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:07.601723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:08.387758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:26:10.912387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:26:10.986550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:26:11.127387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:26:11.205621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:26:11.282684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:26:09.189208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:26:09.295721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

time(s)ST1T2T3T4T5T6T7T8T9T10T11T12Z
00.000000300022.522.522.622.522.723.122.622.525.626.221.921.80.0000
10.083333300022.522.522.622.522.723.122.622.525.626.221.921.80.0000
20.166667300022.522.522.622.522.723.122.622.525.626.221.921.80.0000
30.250000300022.522.522.622.522.723.122.622.525.626.121.921.80.0000
40.333333300022.522.522.622.522.723.122.622.525.626.121.921.80.0000
50.416667300022.522.522.622.522.723.122.622.525.626.121.921.80.0000
60.500000300022.522.522.622.522.723.122.622.525.626.121.921.80.0000
70.583333300022.522.522.622.522.723.122.622.525.626.121.921.80.0000
80.666667300022.522.522.622.522.723.122.622.525.626.121.921.80.0000
90.750000300022.522.522.622.522.723.122.622.525.626.121.921.81.2185

Last rows

time(s)ST1T2T3T4T5T6T7T8T9T10T11T12Z
8271689.250000023.823.823.823.923.923.623.723.727.127.522.622.717.6715
8272689.333333023.823.823.823.923.923.623.723.727.127.522.622.717.6715
8273689.416667023.823.823.823.923.923.623.723.727.127.522.622.717.0620
8274689.500000023.823.823.823.923.923.623.723.727.127.522.622.717.0620
8275689.583333023.823.823.823.923.923.623.723.727.127.522.622.717.0620
8276689.666667023.823.823.823.923.923.623.723.727.127.522.622.717.6715
8277689.750000023.823.823.823.923.923.623.723.727.127.422.622.717.0620
8278689.833333023.823.823.823.923.923.623.723.727.127.422.622.717.0620
8279689.916667023.823.823.823.923.923.623.723.727.127.422.622.717.0620
8280690.000000023.823.823.823.923.923.623.723.727.127.522.722.717.0620